A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006-2017).

Background: In Pakistan, dengue fever has become a major concerning factor, given that it is a relatively new disease compared to malaria. The number of people affected by dengue fever has increased at least 10-fold in the last 15 years in specific areas of Pakistan. Therefore, it is necessary to analyse this disease to reduce or prevent the effects of dengue fever in the region.

Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks.

In this article, a novel approach for training deep neural networks using Bayesian techniques is presented. The Bayesian methodology allows for an easy evaluation of model uncertainty and, additionally, is robust to overfitting. These are commonly the two main problems classical, i.

Portable chamber system for measuring chloroform fluxes from terrestrial environments--methodological challenges.

This study describes a system designed to measure chloroform flux from terrestrial systems, providing a reliable first assessment of the spatial variability of flux over an area. The study takes into account that the variability of ambient air concentrations is unknown. It includes quality assurance procedures, sensitivity assessments, and testing of materials used to ensure that the flux equation used to extrapolate from concentrations to fluxes is sound and that the system does not act as a sink or a source of chloroform.